Target tracking method and apparatus, electronic device, and storage medium

ABSTRACT

Aspects of the disclosure provide an apparatus for target tracking including processing circuitry that obtains a plurality of target instances according to a target detection on image data. Each of the plurality of target instances corresponds to one of a plurality of tracked targets. The processing circuitry determines a plurality of trajectory segments, each of which indicates a trajectory of a subset of the target instances corresponding to a same tracked target. The processing circuitry determines feature information of the plurality of trajectory segments. The processing circuitry performs clustering on specified trajectory segments of the plurality of trajectory segments according to the feature information of the specified trajectory segments, to obtain a type distribution of the specified trajectory segments. The processing circuitry determines, according to the type distribution of the specified trajectory segments, a target tracking result including a same type of the specified trajectory segments.

RELATED APPLICATION

This application is a continuation of International Application No.PCT/CN2018/095473, filed on Jul. 12, 2018, which claims priority toChinese Patent Application No. 201710573025.4, entitled “TARGET TRACKINGMETHOD AND APPARATUS, AND ELECTRONIC DEVICE” filed on Jul. 14, 2017. Theentire disclosures of the prior applications are hereby incorporated byreference in their entirety.

FIELD OF THE TECHNOLOGY

This application relates to the field of computer technologies, and inparticular, to a target tracking method and apparatus, an electronicdevice, and a storage medium.

BACKGROUND OF THE DISCLOSURE

As image capturing devices are becoming more abundant, a large quantityof image capturing devices, such as cameras, can be deployed bothindoors and outdoors to perform target detection (or tracking bydetection) on tracked targets by using image data collected by the imagecapturing devices at any time, thereby tracking a target.

However, in a process of tracking the target, two different trackedtargets may be obtained through target detection because of an attitudechange of the tracked targets (for example, people). Alternatively, atracked target may disappear or reappear due to discontinuous collectionof image data, which still leads to two different tracked targetsobtained through target detection.

The foregoing shows that the related target tracking still has adisadvantage of low accuracy.

SUMMARY

Aspects of the disclosure provide methods and apparatuses for targettracking. In some examples, an apparatus for target tracking includesprocessing circuitry.

The processing circuitry obtains, according to a target detection onimage data, a plurality of target instances. Each of the plurality oftarget instances corresponds to one of a plurality of tracked targets.The processing circuitry further determines a plurality of trajectorysegments. Each of the plurality of trajectory segments indicates atrajectory of a subset of the target instances corresponding to a sametracked target of the plurality of tracked targets. The processingcircuitry determines feature information of the plurality of trajectorysegments. The processing circuitry performs clustering on specifiedtrajectory segments of the plurality of trajectory segments according tothe feature information of the specified trajectory segments, to obtaina type distribution of the specified trajectory segments. The processingcircuitry determines, according to the type distribution of thespecified trajectory segments, a target tracking result including a sametype of the specified trajectory segments.

In some embodiments, the processing circuitry determines target featureinformation of the target instances associated with one of the pluralityof trajectory segments. The processing circuitry determines, accordingto the target feature information, local feature information and globalfeature information of the target instances associated with the one ofthe plurality of trajectory segments. The processing circuitrydetermines the feature information of the one of the plurality oftrajectory segments according to the local feature information and theglobal feature information.

In some embodiments, the processing circuitry obtains one of theplurality of target instances according to annotation information of aplurality of deformable parts of a tracked target of the plurality oftracked targets in the image data. The tracked target corresponds to theone of the plurality of target instances. The processing circuitryobtains, for one of the target instances associated with the one of theplurality of trajectory segments, a visual feature vector and astructure feature vector of the tracked target corresponding to the oneof the target instances. The visual feature vector is a histogramfeature vector that is extracted from the annotation information of theplurality of deformable parts of the tracked target. The structurefeature vector is based on location deviation values between one of theplurality of deformable parts and another one of the plurality ofdeformable parts of the tracked target. The processing circuitrydetermines the target feature information of the one of the targetinstances according to the visual feature vector and the structurefeature vector of the tracked target corresponding to the one of thetarget instances.

In some embodiments, the processing circuitry determines the localfeature information according to the visual feature vector of thetracked target corresponding to the one of the target instancesassociated with the one of the plurality of trajectory segments. Theprocessing circuitry determines an average value of structure featurevectors and a covariance matrix of the structure feature vectors. Thestructure feature vectors corresponds to the target instances associatedwith the one of the plurality of trajectory segments. The processingcircuitry determines the global feature information according to theaverage value and the covariance matrix of the structure featurevectors.

In some embodiments, the processing circuitry calculates, for each of atleast one predefined type of trajectory segment, a likelihood betweenthe respective predefined type of trajectory segment and one of thespecified trajectory segments according to the feature information ofthe one of the specified trajectory segments. The processing circuitrycalculates, according to the likelihoods between the at least onepredefined type of trajectory segment and the one of the specifiedtrajectory segments, a plurality of probabilities that the one of thespecified trajectory segments follows a uniform distribution in the atleast one predefined type of trajectory segment. The processingcircuitry classifies the one of the specified trajectory segments intoone of the at least one predefined type corresponding to a maximumprobability in the plurality of probabilities.

In some embodiments, the processing circuitry determines whether a firsttarget instance associated with the respective predefined type oftrajectory segment and a second target instance associated with the oneof the specified trajectory segments overlap in time. The processingcircuitry determines the likelihood between the respective predefinedtype of trajectory segment and the one of the specified trajectorysegments to be zero when the first target instance and the second targetinstance are determined to overlap in time.

In some embodiments, the processing circuitry obtains a predefinedtrajectory segment associated with the respective predefined type oftrajectory segment. A first target instance associated with thepredefined trajectory segment is closest in time to a second targetinstance associated with the one of the specified trajectory segments.The processing circuitry calculates a local similarity between the oneof the specified trajectory segments and the predefined trajectorysegment associated with the respective predefined type of trajectorysegment according to local feature information of the one of thespecified trajectory segments and local feature information of thepredefined trajectory segment associated with the respective predefinedtype of trajectory segments. The processing circuitry calculates aglobal similarity between the respective predefined type of trajectorysegment and the one of the specified trajectory segments according tothe global feature information of the one of the specified trajectorysegments and a type parameter of the respective predefined type oftrajectory segment. The processing circuitry calculates the likelihoodbetween the respective predefined type of trajectory segment and the oneof the specified trajectory segments according to the local similarityand the global similarity.

In some embodiments, the processing circuitry determines whether aniteration quantity of the clustering satisfies a preset iterationthreshold. The processing circuitry updates the type parameter of therespective predefined type of trajectory segment when the iterationquantity of the clustering is determined not to satisfy the presetiteration threshold. The processing circuitry calculates a likelihoodbetween the updated respective predefined type of trajectory segment andthe one of the specified trajectory segments according to the featureinformation of the one of the specified trajectory segments.

Aspects of the disclosure also provide a non-transitorycomputer-readable medium storing instructions which when executed by atleast one processor cause the at least one processor to perform any ofthe methods for target tracking.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated herein and constitutea part of this specification, illustrate embodiments consistent withthis application and, together with the description, serve to explainthe principles of this application.

FIG. 1 is a schematic diagram of an implementation environment accordingto this application;

FIG. 2 is a schematic block diagram of hardware of a server according toan exemplary embodiment;

FIG. 3 is a flowchart of a target tracking method according to anexemplary embodiment;

FIG. 4 is a flowchart of an embodiment of step 350 in the embodimentcorresponding to FIG. 3;

FIG. 5 is a schematic diagram of a target instance when a tracked targetis a person according to this application;

FIG. 6 is a flowchart of an embodiment of step 351 in the embodimentcorresponding to FIG. 4;

FIG. 7 is a flowchart of an embodiment of step 353 in the embodimentcorresponding to FIG. 4;

FIG. 8 is a flowchart of an embodiment of step 370 in the embodimentcorresponding to FIG. 3;

FIG. 9 is a flowchart of an embodiment of step 371 in the embodimentcorresponding to FIG. 8;

FIG. 10 is a block diagram of a target tracking apparatus according toan exemplary embodiment;

FIG. 11 is a block diagram of an embodiment of a feature informationobtaining module 750 in the embodiment corresponding to FIG. 10;

FIG. 12 is a block diagram of an embodiment of a target featureconstruction unit 751 in the embodiment corresponding to FIG. 11;

FIG. 13 is a block diagram of an embodiment of a trajectory segmentfeature construction unit 753 in the embodiment corresponding to FIG.11;

FIG. 14 is a block diagram of an embodiment of a type distributionobtaining module 770 in the embodiment corresponding to FIG. 10;

FIG. 15 is a block diagram of another embodiment of a type distributionobtaining module 770 in the embodiment corresponding to FIG. 10;

FIG. 16 is a block diagram of an embodiment of a first likelihoodcalculation unit 771 in the embodiment corresponding to FIG. 15; and

FIG. 17 is a block diagram of another embodiment of a type distributionobtaining module 770 in the embodiment corresponding to FIG. 10.

DESCRIPTION OF EMBODIMENTS

Exemplary embodiments are described in detail herein, and examples ofthe exemplary embodiments are shown in the accompanying drawings. Whenthe following descriptions relate to the accompanying drawings, unlessindicated otherwise, same numbers in different accompanying drawingsrepresent same or similar elements. Implementations described in thefollowing exemplary embodiments are examples of the apparatus and methodthat are detailed in claims and that are consistent with some aspects inthis application.

FIG. 1 is a schematic diagram of an implementation environment of atarget tracking method. The implementation environment includes a server100 and several image capturing devices 200 disposed everywhere.

The image capturing device 200 may be an electronic device forcollecting image data, such as cameras, video recorders, video cameras,and the like. Correspondingly, the image data includes but is notlimited to a video, a photo, and the like.

In this implementation environment, the server 100 obtains, byinteracting with the image capturing devices 200, the image datacollected by the image capturing devices 200, and performs targetdetection on tracked targets by using the image data to track a target.

FIG. 2 is a schematic block diagram of hardware of a server 100according to an exemplary embodiment. It is noted that the server 100 isonly an example adapted to this application, and is not intended tosuggest any limitation as to the scope of use of this application. Theserver 100 may not be explained as being dependent on or needing to haveone or more components of the exemplary server 100 shown in FIG. 2.

A hardware structure of the server 100 can vary greatly due to differentconfigurations or performance. As shown in FIG. 2, the server 100includes a power supply 110, an interface 130, at least one storagemedium 150, and at least one central processing unit (CPU) 170.

The power supply 110 is configured to provide an operating voltage foreach hardware device on the server 100.

The interface 130 includes at least one wired or wireless networkinterface 131, at least one serial-parallel conversion interface 133, atleast one input/output interface 135, at least one USB interface 137,and the like, and is configured to communicate with an external device.

The storage medium 150, as a carrier of resource storage, may be arandom storage medium, a magnetic disk, an optical disc, or the like.Resources stored on the storage medium 150 include an operating system151, an application program 153, data 155, and the like, and storagemodes may be transitory storage or permanent storage. The operatingsystem 151 is used for managing and controlling various hardware devicesand the application program 153 on the server 100, and calculating andprocessing the massive data 155 by the central processing unit 170. Theoperating system 151 may be Windows Server™, Mac OS X™, Linux™,FreeBSD™, and the like. The application program 153 is a computerprogram that performs at least one specific task based on the operatingsystem 151, and may include at least one module (not shown in FIG. 2).Each module can contain a series of operation instructions for theserver 100. The data 155 can be photos, pictures, and the like stored onthe magnetic disk.

The central processing unit 170 may include one or more processors(e.g., processing circuitry) and is configured to communicate with thestorage medium 150 through a bus, for calculating and processing themassive data 155 in the storage medium 150.

As described in detail above, the server 100 applicable to thisapplication reads, by using the central processing unit 170, a series ofoperation instructions stored in the storage medium 150, to track atarget.

In addition, the technical solutions of this application can also berealized by a hardware circuit or a hardware circuit in combination witha software instruction. Therefore, realization of this application isnot limited to any specific hardware circuit, software, and acombination thereof.

Referring to FIG. 3, in an exemplary embodiment, a target trackingmethod is applicable to the server 100 in the implementation environmentshown in FIG. 1. The server 100 may have the hardware structure shown inFIG. 2 in the exemplary embodiment.

As shown in FIG. 3, the target tracking method may be performed by theserver 100, and may include the following steps:

Step 310. Obtain image data, and perform target detection on the imagedata to obtain at least one target instance.

The image data includes dynamic image data and static image data. Thedynamic image data refers to a plurality of image frames, such as avideo, while the static image data may be a static image including oneframe, such as a picture. Based on this, the target tracking in thisembodiment may be carried out based on a video including multi-frameimages or a picture of a single-frame image.

The image data may come from image data collected by an image capturingdevice in real time, or may be image data pre-stored in the server. Thatis, the server may process the image data in real time after the imagedata is collected by the image capturing device, or the server maypre-store the image data and then process the image data. For example,the server processes image data when the server processes fewer tasks orat a time specified by an operator. Therefore, the image data obtainedin this application may be image data collected by an image capturingdevice at present, or image data pre-stored in the server, that is,image data obtained by invoking an image capturing devices at ahistorical time, which is not limited herein.

The image capturing devices may be located everywhere, such as clientdevices, for example, in a corner of a ceiling inside a building, on alamp post outside a building, and of an intelligent robot.Correspondingly, the image data may be any image in any scene where animage capturing device is disposed, any image inside a building, or anyimage outside a building, which is not limited herein.

The tracked target refers to any object on an image in any scene, suchas a person, a car, or a mobile phone in the image, which is not limitedherein.

The target detection of the image data is realized by using a targetdetection model. For example, the target detection model may be adeformable-part model, a single Gaussian model, a mixture Gaussianmodel, or the like, which is not limited herein. The target detectionmodel is pre-created by the server before performing target detection onthe image data.

It may be understood that, because the image data is any image in anyscene where an image capturing device is deployed, sometimes, someimages include one tracked target, some images include a plurality oftracked targets, and some images include no tracked target. Therefore,the target instance refers to image data including one tracked target.

It is noted that based on same image data, possibly no target instancecan be obtained, that is, there is no tracked target in an imageindicated by the image data, or a plurality of target instances may beobtained, that is, an image indicated by the image data contains aplurality of tracked targets, and each tracked target corresponds to onetarget instance.

It is further noted that, regardless how many target instances can beobtained from image data, the target instances overlap with each otherin time. That is, a plurality of target instances included in image dataare obtained from image data collected by the image capturing devices ata same collection time.

Therefore, at least one target instance may be obtained through thetarget detection performed on the image data by using the pre-createdtarget detection model, where the at least one target instance containsone tracked target.

Further, because the image data may be a video of a plurality of framesor a picture of a single frame, target detection is performed on theimage data in frame units, that is, one frame of image is used as inputof the target detection model to implement a target detection process ofthe image data.

Step 330. Search the at least one target instance for target instancesincluding a same tracked target, and connect the target instancesincluding the same tracked target to form a trajectory segment.

First, it is noted that, a preset tracking algorithm, such as a KLTalgorithm, may be used for performing global search on all trackedtargets included in many target instances, to track a tracked target,that is, to find same tracked targets in many target instances.

Therefore, target instances including a same tracked target may beobtained by using the preset tracking algorithm, and then all targetinstances including a same tracked target may be connected to form atrajectory segment.

For example, a target instance A1 includes a tracked target A, a targetinstance A2 includes a tracked target B, and a target instance A3includes a tracked target A. Correspondingly, a trajectory segment 1 isformed by connecting the target instances A1 and A3 including thetracked target A, and a trajectory segment 2 is formed by connecting thetarget instance A2 including the tracked target B.

It is understood that the image data is collected in chronologicalorder, that is, the target instances are in chronological order.

Therefore, when a trajectory segment is obtained, all target instancesincluding a same tracked target are connected in chronological order.

Still in the foregoing example, the first target instance of thetrajectory segment 1 is A1, and the second target instance is A3. It isnoted that, as time goes on, a quantity of target instances included inthe trajectory segments increases, for example, the third targetinstance, the fourth target instance, . . . , the last target instanceis added to the trajectory segment 1 in time order.

Step 350. Perform feature construction on the trajectory segment byusing the target instances in the trajectory segment, to obtaintrajectory segment feature information.

The trajectory segment feature information is used for accuratelydescribing an entire and/or a part of the trajectory segment, touniquely identify the trajectory segment in a form of information. It isunderstood that, if the target instances in the trajectory segmentinclude different tracked targets, the trajectory segment are alsodifferent, which makes the trajectory segment feature informationdifferent as well.

Therefore, after a trajectory segment is formed by connecting targetinstances including a same tracked target, corresponding trajectorysegment feature information may be obtained by using the targetinstances in the trajectory segment.

Specifically, the trajectory segment feature information is obtained byperforming feature construction on the trajectory segment by using thetarget instances in the trajectory segment.

Further, the feature construction includes but is not limited to, targetfeature construction on the target instances, and local featureconstruction and global feature construction on the trajectory segment.Correspondingly, the trajectory segment feature information includes butis not limited to target feature information, local feature information,and global feature information.

Still further, the target feature information is related to a trackedtarget included in a corresponding target instance. The local featureinformation is related to at least one target instance in acorresponding trajectory segment. For example, the local featureinformation is related to the first target instance and the last targetinstance in the trajectory segment. The global feature information isrelated to all target instances in a corresponding trajectory segment.

Step 370. Perform clustering on specified trajectory segments accordingto the trajectory segment feature information, to obtain a trajectorysegment type distribution.

In this embodiment, the clustering refers to a process of classifyingthe specified trajectory segments into a plurality of different typesaccording to different tracked targets, that is, each type of trajectorysegments corresponds to a tracked target through clustering.

It is noted that, the specified trajectory segment are trajectorysegments that need to be clustered, which may be all of the trajectorysegments obtained through step 330, or just any few of the trajectorysegments that need to be clustered.

As described above, if target instances in trajectory segments includedifferent tracked targets, the trajectory segments are also different,which leads to different trajectory segment feature information. Inother words, the trajectory segment feature information may be used torepresent a tracked target. In this way, the corresponding trajectorysegment feature information has high similarity or even consistency,regardless that the tracked targets appear in which scenes, that is,which collected image data, or target instances that the tracked targetsare included.

Therefore, the trajectory segments can be clustered through thetrajectory segment feature information. To be specific, for extremelysimilar or even consistent trajectory segment feature information,target instances included in corresponding trajectory segments may beregarded as including a same tracked target, that is, the correspondingtrajectory segments belong to a same type. Otherwise, for non-similar orinconsistent trajectory segment feature information, target instances inthe corresponding trajectory segments may be regarded as includingdifferent tracked targets, that is, the corresponding trajectorysegments belong to different types.

Based on the above, the trajectory segment type distribution refers totrajectory segments included in different types and a quantity of thetrajectory segments. Different types are for different tracked targets.For example, for all trajectory segments belonging to a type A, trackedtargets included in target instances is B, and for all trajectorysegments belonging to a type C, tracked targets included in targetinstances is D.

For example, a Dirichlet mixture model is used for performing clusteringon the specified trajectory segments. Specifically, the trajectorysegment feature information corresponding to the specified trajectorysegments is used as an input of the Dirichlet mixture model. TheDirichlet mixture model is used for performing clustering on thespecified trajectory segments, to output a type of the specifiedtrajectory segments. Further, the trajectory segment type distributionmay be output by inputting the trajectory segment feature informationfor a plurality of specified trajectory segments.

In an embodiment, a clustering algorithm used for performing clusteringon the trajectory segments may be flexibly adjusted to improve targettracking accuracy.

In the foregoing process, exclusive constraints of the target trackingare realized by performing clustering, that is, same tracked targetsdefinitely belong to a same type, which provides a reliable guaranteefor target tracking accuracy simply and effectively.

Step 390. Connect trajectory segments of a same type in the trajectorysegment type distribution to form a target tracking result.

A trajectory segment is formed by connecting at least one targetinstance including a same tracked target. Further, based on theforegoing steps, for trajectory segments classified into a same type,target instances include a same tracked target. Therefore, the targettracking result formed by connecting the trajectory segments of the sametype is also definitely based on a same tracked target. That is, thetarget tracking result corresponds to a unique tracked target, therebyaccurately tracking a target.

Further, a plurality of targets can be tracked for a plurality of targettracking results formed by connecting different types of trajectorysegments.

A connection for the target tracking result is carried out inchronological order of the trajectory segments. For example, the lasttarget instance of the trajectory segment 1 is prior to the first targetinstance of the trajectory segment 2 in time. In this case, that is, thetrajectory segment 1 is earlier than the trajectory segment 2 in time.Correspondingly, in the target tracking result, the trajectory segment 1is connected before the trajectory segment 2 is connected.

Through the foregoing process, under the action of the trajectorysegment feature information, a plurality of trajectory segments may beclassified based on a same tracked target, to obtain a target trackingresult of the tracked target. Therefore, in the process of targettracking, regardless whether the tracked target disappears or reappears,multi-target tracking may be performed on any plurality of trackedtargets according to a requirement.

Referring to FIG. 4, in an exemplary embodiment, step 350 may includethe following steps:

Step 351. Perform target feature construction on the target instances inthe trajectory segment, to obtain target feature information.

The target feature information is used for accurately describing thetarget instance through feature construction of the target instance.Further, the target feature information is related to a tracked targetincluding in a corresponding target instance, thereby uniquelyidentifying the tracked target in a form of information.

It is understood that, different tracked targets leads to differenttarget feature information. For example, if the tracked target is aperson in the image, the target feature information may include a visualfeature vector and a structure feature vector of the person; if thetracked target is a vehicle in the image, the target feature informationmay include a plate number; if the tracked target is a mobile phone inthe image, the target feature information may include a deviceidentification code or a device signal.

Herein, the target feature information is not enumerated herein.Different tracked targets each have corresponding target featureinformation, so as to accurately describe and identify a tracked target.

Step 353. Perform local feature construction and global featureconstruction on the trajectory segment according to the target featureinformation, to obtain local feature information and global featureinformation.

It is noted first that, the local feature information is used foraccurately describing some trajectory segments through local featureconstruction of the trajectory segments.

Further, the local feature information is related to at least one targetinstance in a corresponding trajectory segment. For example, the localfeature information is related to the first target instance and the lasttarget instance in the corresponding trajectory segment.

Therefore, the local feature information may be defined by using thetarget feature information corresponding to the at least one targetinstance. For example, the target feature information includes a visualfeature vector of a tracked target. In this case, a visual featurevector is extracted from the target feature information corresponding tothe at least one target instance, and the visual feature vector is usedas the local feature information.

In addition, the global feature information is used for accuratelydescribing an entire trajectory segment through global featureconstruction of the trajectory segment.

Further, the global feature information is related to all targetinstances in a corresponding trajectory segment.

Therefore, the global feature information may be defined by using thetarget feature information corresponding to each target instance. Stillusing the foregoing example for description, the target featureinformation includes a structure feature vector of the tracked target.In this case, a structure feature vector is extracted from the targetfeature information respectively corresponding to the target instances,and the global feature information is obtained by using the structurefeature vectors.

Step 355. Generate the trajectory segment feature information accordingto the local feature information and the global feature information.

As described above, the trajectory segment feature information is usedfor accurately describing an entire and/or a part of a trajectorysegment. Therefore, after the local feature information and the globalfeature information are obtained, trajectory segment feature informationincluding the local feature information and the global featureinformation can be correspondingly obtained.

The foregoing process provides sufficient basis for a clustering processin which the specified trajectory segments are correspondinglyclassified into different types according to the different trackedtargets. That is, the trajectory segment feature information havingextremely high similarity or even consistency is used for representingsame tracked targets.

Further, in an exemplary embodiment, step 310 of performing targetdetection on the image data to obtain at least one target instance mayinclude the following step:

performing annotation information identification on a plurality ofdeformable parts of a tracked target in the image data by using apre-created deformable-part model, to obtain the at least one targetinstance.

A deformable part of the tracked target corresponding to the targetinstance is identified by using annotation information.

For the image data, target detection is performed by using thedeformable-part model, where an obtained tracked target is a non-rigidtarget. The non-rigid target is a tracked target that deforms in atarget tracking process, for example, a person, an animal, or anotherdeformable object.

Specifically, the tracked target is represented by using thedeformable-part model as a global rectangular frame and a plurality ofpart rectangular frames. The global means an entire tracked target, andthe part means a deformable part of the tracked target.

Images annotated by the global rectangular frame and the partrectangular frames are defined as annotation information in the targetinstance, so that a plurality of deformable parts of the tracked targetare identified by using a plurality of pieces of annotation informationin the target instance.

In an example, the tracked target is a person. As shown in FIG. 5, theperson is represented as one global rectangular frame and six partrectangular frames. The global means an entire person, and the partmeans a deformable part such as the head, the left hand, the right hand,the left leg, the right leg, the left foot and the right foot of theperson. Correspondingly, the deformable part of the person is identifiedby using seven pieces of standard information in the target instance.

In an embodiment, a quantity of part rectangular frames may be flexiblyadjusted to satisfy different requirements for target tracking accuracyin different application scenarios.

Correspondingly, referring to FIG. 6, step 351 may include the followingsteps:

Step 3511. Obtain a visual feature vector and a structure feature vectorof the tracked target.

Specifically, histogram feature vector extraction is performed on aplurality of pieces of annotation information, and an extractedhistogram feature vector is used as the visual feature vector of thetracked target.

The histogram feature vector includes a histogram of oriented gradientsfeature vector and a color histogram feature vector. The histogram oforiented gradients feature vector is used for describing a texturefeature of the tracked target, and the color histogram feature vector isused for describing a color feature of the tracked target.

In a histogram feature vector extraction process, histogram featurevector extraction is essentially performed on a deformable partidentified by using a plurality of pieces of annotation information. Forexample, a person's head is identified by using the annotationinformation. In this case, histogram feature vector extraction isperformed on an image annotated by using a part rectangular frame inwhich the person's head is located.

Based on this, the visual feature vector of the tracked target isdefined by using an extracted histogram feature vector, therebyreflecting external appearance information of the tracked target.

For example, for one piece of the annotation information, the histogramof oriented gradients feature vector is a1, and the color histogramfeature vector is b1. In this case, the visual feature vector isobtained by using the annotation information as {a1, b1}.

By analogy, as an amount of annotation information increases, a lengthof the visual feature vector generated by using the annotationinformation also correspondingly increases, thereby improving trackedtarget describing accuracy.

For example, a visual feature vector obtained by using eight pieces ofannotation information is {a1, b1, a2, b2, a3, b3, a4, b4, a5, b5, a6,b6, a7, b7, a8, b8}.

A deformable part identified by using one of the plurality of pieces ofannotation information is used as an anchor point to calculate locationdeviations between the anchor point and deformable parts identified byusing remaining annotation information, and calculated deviation valuesare used as the structure feature vector of the tracked target.

Still using the foregoing example for description, the annotationinformation is used for identifying a deformable part of a person in thetarget instance. In an embodiment, the deformable part of the person isidentified by using eight pieces of annotation information in the targetinstance. That is, an image annotated by using one global rectangularframe represents the head of the person, and images annotated by usingseven part rectangular frames respectively represent parts of the lefthand, the right hand, the body, the left leg, the right leg, the leftfood, and the right food of the person.

Herein, a person's head is used as an anchor point. There are sevendeviation values obtained through calculation based on the person's headand remaining part of the person, so as to obtain a structure featurevector when the tracked target is a person, thereby reflecting internalstructural information when the tracked target is the person.

For example, the deviation values are c1, c2, c3, c4, c5, c6, and c7respectively. In this case, the structure feature vector is {c1, c2, c3,c4, c5, c6, c7}.

Step 3513. Generate target feature information corresponding to thetarget instances according to the visual feature vector and thestructure feature vector of the tracked target.

After obtaining the visual feature vector and the structure featurevector of the tracked target, the target instance can be accuratelydescribed. That is, the target feature information corresponding to thetarget instance includes the visual feature vector and the structurefeature vector of the tracked target, thereby uniquely identify, in aform of information. that the person is the tracked target.

In the foregoing process, the target feature information is used toreflect the external appearance information and the internal structuralinformation of the tracked target, thereby accurately describing thetracked target, facilitating subsequent accurate target tracking.

Referring to FIG. 7, in an exemplary embodiment, step 353 may includethe following steps:

Step 3531. Extract at least one target instance from the trajectorysegment, and use a visual feature vector in target feature informationcorresponding to the at least one target instance as the local featureinformation corresponding to the trajectory segment.

As described above, the local feature information is related to at leastone target instance in a corresponding trajectory segment. The localfeature information may be defined by using the target featureinformation corresponding to the at least one target instance.

In an embodiment, the local feature information is related to the firsttarget instance and the last target instance in the correspondingtrajectory segment.

Specifically, the first target instance and the last target instance areextracted from the trajectory segment in chronological order, and thevisual feature vector in the target feature information corresponding tothe first target instance and the visual feature vector in the targetfeature information corresponding to the last target instance areobtained, so that the local feature information corresponding to thetrajectory segment includes the foregoing visual feature vectors.

In this process, the local feature information may be regarded asaccurate descriptions of the tracked target included in the at least onetarget instance in the trajectory segment. That is, accurate descriptionof a part of the trajectory segment is implemented, facilitatingsubsequent accurate target tracking.

Step 3533. For the target instances in the trajectory segment, calculatean average value of a structure feature vector in the correspondingtarget feature information, and perform a covariance operation accordingto the structure feature vector to obtain a covariance matrix.

Step 3535. Use the average value and the covariance matrix as the globalfeature information corresponding to the trajectory segment.

As described above, the global feature information is related to alltarget instances in a corresponding trajectory segment. The globalfeature information may be defined by using the target featureinformation corresponding to each target instance.

Specifically, various structure feature vectors in the target featureinformation corresponding to all target instances are calculated for anaverage value and a covariance matrix, and the average value and thecovariance matrix are defined as the global feature informationcorresponding to the trajectory segment.

The structure feature vectors in the target feature informationcorresponding to the target instances are used as elements, and elementsin the covariance matrix are the covariance between the foregoingvarious elements.

In this process, all target instances in the trajectory segment areaveraged and de-correlated by using the global feature information,thereby accurately describing an entire trajectory segment, facilitatingsubsequent accurate target tracking.

Referring to FIG. 8, in an exemplary embodiment, step 370 may includethe following steps:

Step 371. Calculate, for predefined at least one type, likelihoodbetween the at least one type and the specified trajectory segmentsaccording to the trajectory segment feature information.

The type is a set of including at least one trajectory segment. In theset, target instances in all trajectory segments include same trackedtarget.

As described above, for extremely similar or even consistent trajectorysegment feature information, target instances included in correspondingtrajectory segments may be regarded as including a same tracked target,that is, the corresponding trajectory segments belong to a same type.

Otherwise, if a type of trajectory segments and the specified trajectorysegments have extremely similar or even consistent trajectory segmentfeature information, the specified trajectory segments may possiblybelongs to the type.

Therefore, before the trajectory segment clustering, the likelihoodbetween each type of trajectory segments and the specified trajectorysegments needs to be obtained according to the trajectory segmentfeature information, to learn whether extremely similar or evenconsistent trajectory segment feature information exists between thespecified trajectory segments and each trajectory segment in the type.

Step 373. Calculate, according to the likelihood, a probability that thespecified trajectory segments follows a uniform distribution in the atleast one type.

It is noted that herein, after the likelihood between each type oftrajectory segments and the specified trajectory segments is calculated,normalization processing is first performed on the calculatedlikelihood, to ensure all likelihood for probability calculating are ofa same quantity level, thereby improving probability calculatingaccuracy, and further improving target tracking accuracy.

Step 375. Classify the specified trajectory segments into a type ofcorresponding to a maximum probability.

Step 377. Complete clustering of the specified trajectory segments toform the trajectory segment type distribution.

When the specified trajectory segments that need to be clustered areclassified into a same type or different types, clustering is completedonce. In this case, a clustering result is a trajectory segment typedistribution.

Further, to improve target tracking accuracy, a plurality of times ofclustering may be performed, and the last clustering result is used as atrajectory segment type distribution. A quantity of iterations of theclustering can be flexibly adjusted according to a requirement in anactual application scenario.

In an exemplary embodiment, before step 371, the method may furtherinclude the following steps:

for target instances included in a type of trajectory segments,determining whether a trajectory segment in the type of trajectorysegments and the specified trajectory segments include target instancestemporally overlapping with each other; and

if the trajectory segment exists, setting likelihood between the type oftrajectory segments and the specified trajectory segments to zero.

As described above, the target instances overlapping with each other intime are obtained from image data collected by image capturing devicesat a same collection time. That is, these target instances are derivedfrom the same image data.

It may be understood that same image data may include more than onetracked target. Correspondingly, after the target detection, a pluralityof target instances containing different tracked targets may beobtained. The plurality of target instances are connected to form aplurality of trajectory segments, making it impossible that thetrajectory segments belong to a same type.

Therefore, if the specified trajectory segments and a trajectory segmentin one type have target instances that overlap with each other in time,it indicates that the specified trajectory segments cannot belong to thetype. In other words, the likelihood between the type of trajectorysegments and the specified trajectory segments is necessarily zero.

As can be seen from the above, if a trajectory segment in one typeincludes a target instance that overlap with a target instance in thespecified trajectory segments in time, there is no need to calculate thelikelihood between the type of trajectory segments and the specifiedtrajectory segments, and the between the type of trajectory segments andthe specified trajectory segments can be directly to set to zero.

In combination with the foregoing embodiment, exclusive constraints forthe tracked target, that is, trajectory segments corresponding todifferent tracked targets in same image data belong to different types,providing a guarantee for target tracking accuracy simply andeffectively.

Referring to FIG. 9, in an exemplary embodiment, step 371 may includethe following steps:

Step 3711. For at least one target instance in the specified trajectorysegment, obtain, from a type of trajectory segments, a trajectorysegment to which a target instance closest to the at least one targetinstance in time belongs.

In an embodiment, the likelihood calculation is calculated by using thefirst and the last target instances in a specified trajectory segment.

It is understood that a connection of the target tracking result iscarried out in chronological order of the trajectory segments.Therefore, each trajectory segment in a same type is bound to have atime sequence, and target instances in each trajectory segment also havea time sequence. In other words, the last target instance connected inthe preceding trajectory segment is prior to the first target instanceconnected in the following trajectory segment on the time axis.

Correspondingly, in one type, a target instance closest to the firsttarget instance in the specified trajectory segments in time is the lasttarget instance in a trajectory segment of the type.

A target instance closest to the last target instance in the specifiedtrajectory segments in time is the first target instance in a trajectorysegment of the type.

Step 3713. Calculate local similarity between the specified trajectorysegments and the obtained trajectory segment according to local featureinformation respectively corresponding to the trajectory segments.

It is noted that the trajectory segments are the specified trajectorysegments and the obtained trajectory segment. Correspondingly, the localfeature information corresponding to the trajectory segmentsrespectively is local feature information of the specified trajectorysegments and local feature information of the obtained trajectorysegment.

Step 3715. Calculate global similarity between the type of trajectorysegments and the specified trajectory segments according to globalfeature information corresponding to the specified trajectory segmentsand a type parameter of the type of trajectory segments.

Step 3717. Calculate likelihood between the type of trajectory segmentsand the specified trajectory segments by using the local similarity andthe global similarity.

Specifically, for the specified trajectory segments and the type, thelikelihood between them can be calculated as follows:f(x,|Φ _(k) ,x _(k,g))∝s(A _(i) ^(head) ,A _(k,m) ^(tail))s(A _(i)^(tail) ,A _(k,n) ^(head))p(D _(i) ,V _(i);Φ_(k))

where f represents the likelihood between the type of trajectorysegments and the specified trajectory segments; and

x_(i) represents an i^(th) specified trajectory segment, (ϕ_(k),x_(k,[ ])) represents the type, the type is a k^(th) type in thetrajectory segment type distribution, and the k^(th) type includesseveral trajectory segments, which is represented by [ ].

In the first term s, A_(i) ^(tail) represents a visual feature vector intarget feature information corresponding to the first target instance inthe i^(th) specified trajectory segment, and A_(k,n) ^(head) representsa visual feature vector in target feature information corresponding tothe last target instance in an m^(th) trajectory segment of the k^(th)type.

In the second term s, A_(i) ^(tail) represents a visual feature vectorin target feature information corresponding to the last target instancein the i^(th) specified trajectory segment, and A_(k,n) ^(head) headrepresents a visual feature vector in target feature informationcorresponding to the first target instance in an n^(th) trajectorysegment of the k^(th) type.

In the third item p, D_(i) and V_(i) respectively represent an averagevalue and a covariance matrix in the global feature informationcorresponding to the i^(th) specified trajectory segment. ϕ_(k) is atype parameter of the k^(th) type, where the type parameter is obtainedfrom Gaussian model modeling by using an average value and a covariancematrix in the global feature information corresponding to all trajectorysegments [ ] of the k^(th) type.

Further, the function s represents calculation, for the foregoing twotarget instances, of similarity between histogram feature vectors invisual feature vectors corresponding to the two target instances. Then,all calculated histogram feature vectors are accumulated to obtain localsimilarity between the i^(th) specified trajectory segment and them^(th) trajectory segment of the k^(th) type and between the i^(th)specified trajectory segment and the n^(th) trajectory segment of thek^(th) type. The histogram feature vector includes a histogram oforiented gradients feature vector and a color histogram feature vector.

The function p represents that Gaussian model modeling was carried outon an average value and a covariance matrix in the global featureinformation corresponding to all trajectory segments [ ] of the k^(th)type and the i^(th) specified trajectory segment, next a distancebetween the two Gaussian models obtained through the modeling wascompared, and then comparison results are converted into globalsimilarity between the specified trajectory segments and the type.

Further, after calculating the local similarity s1 between the i^(th)specified trajectory segment and the m^(th) trajectory segment of thek^(th) type, the local similarity s2 between the i^(th) specifiedtrajectory segment and the n^(th) trajectory segment of the k^(th) type,and the global similarity p between the i^(th) specified trajectorysegment and the k^(th) type, the likelihood f between the i^(th)specified trajectory segment and the k^(th) type can be calculated byusing the foregoing formula.

In an exemplary embodiment, before step 377, the method may furtherinclude the following step:

determining whether an iteration quantity of the clustering satisfies apreset iteration threshold.

For example, the preset iteration threshold is set to 500 times.Certainly, the preset iteration threshold can be set flexibly accordingto an actual requirement. For example, to improve target trackingaccuracy, the preset iteration threshold is increased, and to reduceprocessing pressure on the server, the preset iteration threshold isreduced.

If the quantity of iterations of the clustering satisfies the presetiteration threshold, the iterative process of the clustering is stoppedand a result obtained through the last clustering is used as atrajectory segment type distribution, that is, step 377 is performed.

Otherwise, if the iteration quantity of the clustering does not satisfythe preset iteration threshold, type parameter update is triggered, andfor at least one type for which a type parameter has been updated,likelihood between the at least one type and the specified trajectorysegments is calculated according to the trajectory segment featureinformation. That is, step 371 is returned to, until the iterationquantity of the clustering satisfies the preset iteration threshold.

In addition, according to different application scenarios, the stopcondition of the cyclic iteration can be flexibly set, or the cycliciteration can be stopped when a calculation time reaches a presetcalculation time, or the cyclic iteration is stopped when a clusteringresult remains unchanged.

Under the action of the foregoing embodiment, clustering accuracy isimproved through cyclic iteration, making tracked targets based on atarget tracking result more consistent, thereby improving targettracking accuracy.

The following is an apparatus embodiment in this application. Theapparatus may be configured to implement the target tracking method inthis application. For details not disclosed in the apparatus embodimentin this application, refer to the embodiment of the target trackingmethod in this application.

Referring to FIG. 10, in an exemplary embodiment, a target trackingapparatus 700 includes, but is not limited to, a target instanceobtaining module 710, a trajectory segment obtaining module 730, afeature information obtaining module 750, a type distribution obtainingmodule 770, and a tracking result obtaining module 790.

The target instance obtaining module 710 is configured to: obtain imagedata, and perform target detection on the image data to obtain at leastone target instance. Each target instance corresponds to one trackedtarget.

The trajectory segment obtaining module 730 is configured to: search theat least one target instance for target instances including a sametracked target, and connect the target instances including the sametracked target to form a trajectory segment.

The feature information obtaining module 750 is configured to performfeature construction on the trajectory segment by using the targetinstances in the trajectory segment, to obtain trajectory segmentfeature information.

The type distribution obtaining module 770 is configured to performclustering on specified trajectory segments according to the trajectorysegment feature information, to obtain a trajectory segment typedistribution.

The tracking result obtaining module 790 is configured to connecttrajectory segments of a same type in the trajectory segment typedistribution to form a target tracking result.

Referring to FIG. 11, in an exemplary embodiment, the featureinformation obtaining module 750 includes, but is not limited to, atarget feature construction unit 751, a trajectory segment featureconstruction unit 753, and a feature information defining unit 755.

The target feature construction unit 751 is configured to perform targetfeature construction on the target instances in the trajectory segment,to obtain target feature information.

The trajectory segment feature construction unit 753 is configured toperform local feature construction and global feature construction onthe trajectory segment according to the target feature information, toobtain local feature information and global feature information.

The feature information defining unit 755 is configured to generate thetrajectory segment feature information according to the local featureinformation and the global feature information.

In an exemplary embodiment, the target instance obtaining moduleincludes an annotation information identification unit.

The annotation information identification unit is configured to performannotation information identification on a plurality of deformable partsof a tracked target in the image data by using a pre-createddeformable-part model, to obtain the at least one target instance. Adeformable part of the tracked target corresponding to the targetinstance is identified by using annotation information.

Correspondingly, referring to FIG. 12, the target feature constructionunit 751 includes, but is not limited to, a feature vector obtainingsubunit 7511 and a feature information forming subunit 7513.

The feature vector obtaining subunit 7511 is configured to obtain avisual feature vector and a structure feature vector of the trackedtarget.

Specifically, histogram feature vector extraction is performed on aplurality of pieces of annotation information, and an extractedhistogram feature vector is used as the visual feature vector of thetracked target. A deformable part identified by using one of theplurality of pieces of annotation information is used as an anchor pointto respectively calculate location deviations between the anchor pointand deformable parts identified by using remaining annotationinformation, and calculated deviation values are used as the structurefeature vector of the tracked target.

The feature information forming subunit 7513 is configured to generatetarget feature information corresponding to the target instancesaccording to the visual feature vector and the structure feature vectorof the tracked target.

Referring to FIG. 13, in an exemplary embodiment, the trajectory segmentfeature construction unit 753 includes, but is not limited to, a localfeature information defining subunit 7531, a structure feature vectorcalculation subunit 7533, and a global feature information definingsubunit 7535.

The local feature information defining subunit 7531 is configured to:extract at least one target instance from the trajectory segment, anduse a visual feature vector in target feature information correspondingto the at least one target instance as the local feature informationcorresponding to the trajectory segment.

The structure feature vector calculation subunit 7533 is configured to:for the target instances in the trajectory segment, calculate an averagevalue of a structure feature vector in the corresponding target featureinformation, and perform a covariance operation according to thestructure feature vector to obtain a covariance matrix.

The global feature information defining subunit 7535 is configured touse the average value and the covariance matrix as the global featureinformation corresponding to the trajectory segment.

Referring to FIG. 14, in an exemplary embodiment, the type distributionobtaining module 770 includes, but is not limited to, a first likelihoodcalculation unit 771, a probability calculation unit 773, a clusteringunit 775, and a type distribution forming unit 777.

The first likelihood calculation unit 771 is configured to calculate,for predefined at least one type, likelihood between the at least onetype and the specified trajectory segments according to the trajectorysegment feature information.

The probability calculation unit 773 is configured to calculate,according to the likelihood, a probability that the specified trajectorysegments follows a uniform distribution in the at least one type.

The clustering unit 775 is configured to classify the specifiedtrajectory segments into a type of corresponding to a maximumprobability.

The type distribution forming unit 777 is configured to completeclustering of the specified trajectory segments to form the trajectorysegment type distribution.

Referring to FIG. 15, in an exemplary embodiment, the type distributionobtaining module 770 further includes, but is not limited to, anoverlapping determining unit 810 and a likelihood setting unit 830.

The overlapping determining unit 810 is configured to: for targetinstances included in a type of trajectory segments, determine whether atrajectory segment in the type of trajectory segments and the specifiedtrajectory segments include target instances temporally overlapping witheach other. If the trajectory segment exists, the likelihood settingunit is notified.

The likelihood setting unit 830 is configured to set likelihood betweenthe type of trajectory segments and the specified trajectory segments tozero.

Referring to FIG. 16, in an exemplary embodiment, the first likelihoodcalculation unit 771 includes, but is not limited to, a trajectorysegment feature vector obtaining subunit 7711, a local similaritycalculation subunit 7713, a global similarity calculation subunit 7715,and a likelihood calculation subunit 7717.

The trajectory segment feature vector obtaining subunit 7711 isconfigured to: for at least one target instance in the specifiedtrajectory segment, obtain, from a type of trajectory segments, atrajectory segment to which a target instance closest to the at leastone target instance in time belongs.

The local similarity calculation subunit 7713 is configured to calculatelocal similarity between the specified trajectory segments and theobtained trajectory segment according to local feature informationrespectively corresponding to the trajectory segments.

The global similarity calculation subunit 7715 is configured tocalculate global similarity between the type of trajectory segments andthe specified trajectory segments according to global featureinformation corresponding to the specified trajectory segments and atype parameter of the type of trajectory segments.

The likelihood calculation subunit 7717 is configured to calculatelikelihood between the type of trajectory segments and the specifiedtrajectory segments by using the local similarity and the globalsimilarity.

Referring to FIG. 17, in an exemplary embodiment, the type distributionobtaining module 770 further includes, but is not limited to, aniteration determining unit 910 and a second likelihood calculation unit930.

The iteration determining unit 910 is configured to determine whether aniteration quantity of the clustering satisfies a preset iterationthreshold. If the iteration quantity of the clustering does not satisfythe preset iteration threshold, the second likelihood calculation unitis notified.

The second likelihood calculation unit 930 is configured to: trigger toperform type parameter update, and for at least one type for which atype parameter has been updated, calculate likelihood between the atleast one type and the specified trajectory segments according to thetrajectory segment feature information.

It is noted that, when the target tracking apparatus provided in theforegoing embodiment performs target tracking, the divisions of theforegoing functional modules are described by using an example. Duringactual application, the foregoing functions may be allocated to andcompleted by different functional modules according to requirements,that is, the internal structure of the target tracking apparatus isdivided into different functional modules, to complete all or some ofthe foregoing described functions.

In addition, the target tracking apparatus provided in the foregoingembodiment and the embodiment of the target tracking method belong to asame ideal. Specific operations manners of the modules have beendescribed in detail in the method embodiment, and the details are notdescribed herein again.

In an exemplary embodiment, an electronic device includes one or moreprocessors and one or more memories.

The memory can be a non-transitory computer-readable medium that storescomputer-readable instructions, the computer-readable instructions, whenexecuted by the processor, implementing the target tracking method inthe foregoing embodiment.

In an exemplary embodiment, a computer-readable storage medium stores acomputer program, the computer program, when executed by a processor,implementing the target tracking method in the foregoing embodiment.

The foregoing descriptions are exemplary embodiments of thisapplication, and are not intended to limit the embodiments of thisapplication. A person of ordinary skill in the art can makecorresponding modifications and variations with ease without departingfrom the spirit and scope of the embodiments of this application.Therefore, the protection scope of this application shall be subject tothe protection scope of the claims.

What is claimed is:
 1. A target tracking method, comprising: obtaining,by processing circuitry of an apparatus and according to a targetdetection on image data, a plurality of target instances, each of theplurality of target instances corresponding to one of a plurality oftracked targets; determining, by the processing circuitry, a pluralityof trajectory segments, each of the plurality of trajectory segmentsindicating a trajectory of a subset of the target instancescorresponding to a same tracked target of the plurality of trackedtargets; determining, by the processing circuitry, feature informationof the plurality of trajectory segments; classifying, by the processingcircuitry, a type of each specified trajectory segment of the pluralityof trajectory segments into one of a plurality of predefined types oftrajectory segment according to the feature information of therespective specified trajectory segment, the one of the plurality ofpredefined types of trajectory segment corresponding to a maximumprobability in a plurality of probabilities of the respective specifiedtrajectory segment that follows a uniform distribution, and each of theplurality of probabilities corresponding to a respective one of theplurality of predefined types of trajectory segment; and determining, bythe processing circuitry and according to the types of the specifiedtrajectory segments, a target tracking result including a same type ofthe specified trajectory segments.
 2. The method according to claim 1,wherein the determining the feature information of the plurality oftrajectory segments comprises: determining, by the processing circuitry,target feature information of the target instances associated with oneof the plurality of trajectory segments; determining, by the processingcircuitry and according to the target feature information, local featureinformation and global feature information of the target instancesassociated with the one of the plurality of trajectory segments; anddetermining, by the processing circuitry, the feature information of theone of the plurality of trajectory segments according to the localfeature information and the global feature information.
 3. The methodaccording to claim 2, wherein the obtaining the plurality of targetinstances includes obtaining, by the processing circuitry, one of theplurality of target instances according to annotation information of aplurality of deformable parts of a tracked target of the plurality oftracked targets in the image data, the tracked target corresponding tothe one of the plurality of target instances; and the determining thetarget feature information of the target instances associated with theone of the plurality of trajectory segments includes obtaining, by theprocessing circuitry and for one of the target instances associated withthe one of the plurality of trajectory segments, a visual feature vectorand a structure feature vector of the tracked target corresponding tothe one of the target instances, the visual feature vector being ahistogram feature vector that is extracted from the annotationinformation of the plurality of deformable parts of the tracked target,and the structure feature vector being based on location deviationvalues between one of the plurality of deformable parts and another oneof the plurality of deformable parts of the tracked target, anddetermining, by the processing circuitry, the target feature informationof the one of the target instances according to the visual featurevector and the structure feature vector of the tracked targetcorresponding to the one of the target instances.
 4. The methodaccording to claim 3, wherein the determining the local featureinformation and the global feature information of the target instancesassociated with the one of the plurality of trajectory segmentscomprises: determining, by the processing circuitry, the local featureinformation according to the visual feature vector of the tracked targetcorresponding to the one of the target instances associated with the oneof the plurality of trajectory segments; determining, by the processingcircuitry, an average value of structure feature vectors and acovariance matrix of the structure feature vectors, the structurefeature vectors corresponding to the target instances associated withthe one of the plurality of trajectory segments; and determining, by theprocessing circuitry, the global feature information according to theaverage value and the covariance matrix of the structure featurevectors.
 5. The method according to claim 1, wherein the classifyingcomprises: calculating, by the processing circuitry and for each of theplurality of predefined types of trajectory segment, a likelihoodbetween the respective predefined type of trajectory segment and one ofthe specified trajectory segments according to the feature informationof the one of the specified trajectory segments; calculating, by theprocessing circuitry and according to the likelihoods between theplurality of predefined types of trajectory segment and the one of thespecified trajectory segments, the plurality of probabilities of the oneof the specified trajectory segments that follows the uniformdistribution, each of the plurality of probabilities corresponding to arespective one of the plurality of predefined types of trajectorysegment; and classifying, by the processing circuitry, the one of thespecified trajectory segments into the one of the plurality ofpredefined types corresponding to the maximum probability in theplurality of probabilities.
 6. The method according to claim 5, whereinbefore the calculating the likelihood between the respective predefinedtype of trajectory segment and the one of the specified trajectorysegments, the method further comprises: determining, by the processingcircuitry, whether a first target instance associated with therespective predefined type of trajectory segment and a second targetinstance associated with the one of the specified trajectory segmentsoverlap in time; and determining, by the processing circuitry, thelikelihood between the respective predefined type of trajectory segmentand the one of the specified trajectory segments to be zero when thefirst target instance and the second target instance are determined tooverlap in time.
 7. The method according to claim 5, wherein thecalculating the likelihood between the respective predefined type oftrajectory segment and the one of the specified trajectory segmentscomprises: obtaining, by the processing circuitry, a predefinedtrajectory segment associated with the respective predefined type oftrajectory segment, a first target instance associated with thepredefined trajectory segment being closest in time to a second targetinstance associated with the one of the specified trajectory segments;calculating, by the processing circuitry, a local similarity between theone of the specified trajectory segments and the predefined trajectorysegment associated with the respective predefined type of trajectorysegment according to local feature information of the one of thespecified trajectory segments and local feature information of thepredefined trajectory segment associated with the respective predefinedtype of trajectory segment; calculating, by the processing circuitry, aglobal similarity between the respective predefined type of trajectorysegment and the one of the specified trajectory segments according tothe global feature information of the one of the specified trajectorysegments and a type parameter of the respective predefined type oftrajectory segment; and calculating, by the processing circuitry, thelikelihood between the respective predefined type of trajectory segmentand the one of the specified trajectory segments according to the localsimilarity and the global similarity.
 8. The method according to claim7, further comprising: determining, by the processing circuitry, whetheran iteration quantity of the classifying satisfies a preset iterationthreshold; updating, by the processing circuitry, the type parameter ofthe respective predefined type of trajectory segment when the iterationquantity of the classifying is determined not to satisfy the presetiteration threshold; and calculating, by the processing circuitry, alikelihood between the updated respective predefined type of trajectorysegment and the one of the specified trajectory segments according tothe feature information of the one of the specified trajectory segments.9. A target tracking apparatus, comprising processing circuitryconfigured to: obtain, according to a target detection on image data, aplurality of target instances, each of the plurality of target instancescorresponding to one of a plurality of tracked targets; determine aplurality of trajectory segments, each of the plurality of trajectorysegments indicating a trajectory of a subset of the target instancescorresponding to a same tracked target of the plurality of trackedtargets; determine feature information of the plurality of trajectorysegments; classify a type of each specified trajectory segment of theplurality of trajectory segments into one of a plurality of predefinedtypes of trajectory segment according to the feature information of therespective specified trajectory segment, the one of the plurality ofpredefined types of trajectory segment corresponding to a maximumprobability in a plurality of probabilities of the respective specifiedtrajectory segment that follows a uniform distribution, and each of theplurality of probabilities corresponding to a respective one of theplurality of predefined types of trajectory segment; and determine,according to the types of the specified trajectory segments, a targettracking result including a same type of the specified trajectorysegments.
 10. The apparatus according to claim 9, wherein the processingcircuitry is further configured to: determine target feature informationof the target instances associated with one of the plurality oftrajectory segments; determine, according to the target featureinformation, local feature information and global feature information ofthe target instances associated with the one of the plurality oftrajectory segments; and determine the feature information of the one ofthe plurality of trajectory segments according to the local featureinformation and the global feature information.
 11. The apparatusaccording to claim 10, wherein the processing circuitry is furtherconfigured to: obtain one of the plurality of target instances accordingto annotation information of a plurality of deformable parts of atracked target of the plurality of tracked targets in the image data,the tracked target corresponding to the one of the plurality of targetinstances; obtain, for one of the target instances associated with theone of the plurality of trajectory segments, a visual feature vector anda structure feature vector of the tracked target corresponding to theone of the target instances, the visual feature vector being a histogramfeature vector that is extracted from the annotation information of theplurality of deformable parts of the tracked target, and the structurefeature vector being based on location deviation values between one ofthe plurality of deformable parts and another one of the plurality ofdeformable parts of the tracked target; and determine the target featureinformation of the one of the target instances according to the visualfeature vector and the structure feature vector of the tracked targetcorresponding to the one of the target instances.
 12. The apparatusaccording to claim 11, wherein the processing circuitry is furtherconfigured to: determine the local feature information according to thevisual feature vector of the tracked target corresponding to the one ofthe target instances associated with the one of the plurality oftrajectory segments; determine an average value of structure featurevectors and a covariance matrix of the structure feature vectors, thestructure feature vectors corresponding to the target instancesassociated with the one of the plurality of trajectory segments; anddetermine the global feature information according to the average valueand the covariance matrix of the structure feature vectors.
 13. Theapparatus according to claim 9, wherein the processing circuitry isfurther configured to: calculate, for each of the plurality ofpredefined types of trajectory segment, a likelihood between therespective predefined type of trajectory segment and one of thespecified trajectory segments according to the feature information ofthe one of the specified trajectory segments; calculate, according tothe likelihoods between the plurality of predefined types of trajectorysegment and the one of the specified trajectory segments, the pluralityof probabilities of the one of the specified trajectory segments thatfollows the uniform distribution, each of the plurality of probabilitiescorresponding to a respective one of the plurality of predefined typesof trajectory segment; and classify the one of the specified trajectorysegments into the one of the plurality of predefined types correspondingto the maximum probability in the plurality of probabilities.
 14. Theapparatus according to claim 13, wherein the processing circuitry isfurther configured to: determine whether a first target instanceassociated with the respective predefined type of trajectory segment anda second target instance associated with the one of the specifiedtrajectory segments overlap in time; and determine the likelihoodbetween the respective predefined type of trajectory segment and the oneof the specified trajectory segments to be zero when the first targetinstance and the second target instance are determined to overlap intime.
 15. The apparatus according to claim 13, wherein the processingcircuitry is further configured to: obtain a predefined trajectorysegment associated with the respective predefined type of trajectorysegment, a first target instance associated with the predefinedtrajectory segment being closest in time to a second target instanceassociated with the one of the specified trajectory segments; calculatea local similarity between the one of the specified trajectory segmentsand the predefined trajectory segment associated with the respectivepredefined type of trajectory segment according to local featureinformation of the one of the specified trajectory segments and localfeature information of the predefined trajectory segment associated withthe respective predefined type of trajectory segment; calculate a globalsimilarity between the respective predefined type of trajectory segmentand the one of the specified trajectory segments according to the globalfeature information of the one of the specified trajectory segments anda type parameter of the respective predefined type of trajectorysegment; and calculate the likelihood between the respective predefinedtype of trajectory segment and the one of the specified trajectorysegments according to the local similarity and the global similarity.16. The apparatus according to claim 15, wherein the processingcircuitry is further configured to: determine whether an iterationquantity of the classifying satisfies a preset iteration threshold;update the type parameter of the respective predefined type oftrajectory segment when the iteration quantity of the classifying isdetermined not to satisfy the preset iteration threshold; and calculatea likelihood between the updated respective predefined type oftrajectory segment and the one of the specified trajectory segmentsaccording to the feature information of the one of the specifiedtrajectory segments.
 17. A non-transitory computer-readable mediumstoring a program executable by at least one processor to perform:obtaining, according to a target detection on image data, a plurality oftarget instances, each of the plurality of target instancescorresponding to one of a plurality of tracked targets; determining aplurality of trajectory segments, each of the plurality of trajectorysegments indicating a trajectory of a subset of the target instancescorresponding to a same tracked target of the plurality of trackedtargets; determining feature information of the plurality of trajectorysegments; classifying a type of each specified trajectory segment of theplurality of trajectory segments into one of a plurality of predefinedtypes of trajectory segment according to the feature information of therespective specified trajectory segment, the one of the plurality ofpredefined types of trajectory segment corresponding to a maximumprobability in a plurality of probabilities of the respective specifiedtrajectory segment that follows a uniform distribution, and each of theplurality of probabilities corresponding to a respective one of theplurality of predefined types of trajectory segment; and determining,according to the types of the specified trajectory segments, a targettracking result including a same type of the specified trajectorysegments.
 18. The non-transitory computer-readable storage mediumaccording to claim 17, wherein the program is executable by the at leastone processor to perform: determining target feature information of thetarget instances associated with one of the plurality of trajectorysegments; determining, according to the target feature information,local feature information and global feature information of the targetinstances associated with the one of the plurality of trajectorysegments; and determining the feature information of the one of theplurality of trajectory segments according to the local featureinformation and the global feature information.
 19. The non-transitorycomputer-readable storage medium according to claim 18, wherein theprogram is executable by the at least one processor to perform:obtaining one of the plurality of target instances according toannotation information of a plurality of deformable parts of a trackedtarget of the plurality of tracked targets in the image data, thetracked target corresponding to the one of the plurality of targetinstances; obtaining, for one of the target instances associated withthe one of the plurality of trajectory segments, a visual feature vectorand a structure feature vector of the tracked target corresponding tothe one of the target instances, the visual feature vector being ahistogram feature vector that is extracted from the annotationinformation of the plurality of deformable parts of the tracked target,and the structure feature vector being based on location deviationvalues between one of the plurality of deformable parts and another oneof the plurality of deformable parts of the tracked target; anddetermining the target feature information of the one of the targetinstances according to the visual feature vector and the structurefeature vector of the tracked target corresponding to the one of thetarget instances.
 20. The non-transitory computer-readable storagemedium according to claim 19, wherein the program is executable by theat least one processor to perform: determining the local featureinformation according to the visual feature vector of the tracked targetcorresponding to the one of the target instances associated with the oneof the plurality of trajectory segments; determining an average value ofstructure feature vectors and a covariance matrix of the structurefeature vectors, the structure feature vectors corresponding to thetarget instances associated with the one of the plurality of trajectorysegments; and determining the global feature information according tothe average value and the covariance matrix of the structure featurevectors.